Multi-class Discriminant Kernel Learning via Convex Programming

By Jieping Ye, Shuiwang Ji, and Jianhui Chen. Journal of Machine Learning Research, 9(Apr):719-758, 2008.

Source codes in MATLAB:

• Binary-class case
• SDP1 (Kernel learning by Semi-definite Programming, with regularization parameter fixed at 0.0005 in our experiments)
• SDP2 (Joint kernel and regularization parameters learning by Semi-definite Programming)
• QCQP1 (Kernel learning by Quadratically Constrained Quadratic programming, with regularization parameter fixed at 0.0005 in our experiments)
• QCQP2 (Joint kernel and regularization parameters learning by Quadratically Constrained Quadratic programming)
• SILP1 (Kernel learning by Semi-infinite Linear Programming with regularization parameter fixed at 0.0005 and tolerance parameter fixed at 0.0005 in our experiments)
• SILP2 (Joint kernel and regularization parameters learning by Semi-infinite Linear Programming)
• Multi-class case
• SDP1  (Kernel learning by Semi-definite Programming, with regularization parameter fixed at 0.0005 in our experiments)
• SDP2 (Joint kernel and regularization parameters learning by Semi-definite Programming)
• QCQP1 (Kernel learning by Quadratically Constrained Quadratic programming, with regularization parameter fixed at 0.0005 in our experiments)
• QCQP2 (Joint kernel and regularization parameters learning by Quadratically Constrained Quadratic programming)
• SILP1 (Kernel learning by Semi-infinite Linear Programming with regularization parameter fixed at 0.0005 and tolerance parameter fixed at 0.0005 in our experiments)
• SILP2 (Joint kernel and regularization parameters learning by Semi-infinite Linear Programming)

Link to SeDuMi package

Link to MOSEK package